Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations205439
Missing cells482
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.9 MiB
Average record size in memory668.2 B

Variable types

Text6
Categorical4
Numeric7

Alerts

2020 Census Tract is highly overall correlated with StateHigh correlation
CAFV is highly overall correlated with E.V_Type and 2 other fieldsHigh correlation
E.V_Type is highly overall correlated with CAFV and 2 other fieldsHigh correlation
Electric Range is highly overall correlated with CAFV and 2 other fieldsHigh correlation
Legislative District is highly overall correlated with StateHigh correlation
Make is highly overall correlated with CAFV and 1 other fieldsHigh correlation
Model Year is highly overall correlated with Electric RangeHigh correlation
Postal Code is highly overall correlated with StateHigh correlation
State is highly overall correlated with 2020 Census Tract and 2 other fieldsHigh correlation
State is highly imbalanced (99.4%)Imbalance
Postal Code is highly skewed (γ1 = -30.21116405)Skewed
2020 Census Tract is highly skewed (γ1 = -27.12221244)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 114172 (55.6%) zerosZeros
Base MSRP has 202114 (98.4%) zerosZeros

Reproduction

Analysis started2024-10-16 13:42:37.200960
Analysis finished2024-10-16 13:43:09.463253
Duration32.26 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct12140
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
2024-10-16T19:13:10.203240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2054390
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2250 ?
Unique (%)1.1%

Sample

1st rowJTMAB3FV3P
2nd row1N4AZ1CP6J
3rd row5YJ3E1EA4L
4th row1N4AZ0CP8E
5th row1G1FX6S00H
ValueCountFrequency (%)
7saygdee6p 1221
 
0.6%
7saygdee7p 1217
 
0.6%
7saygdeexp 1181
 
0.6%
7saygdee5p 1176
 
0.6%
7saygdee8p 1174
 
0.6%
7saygdee9p 1143
 
0.6%
7saygdee0p 1142
 
0.6%
7saygdee2p 1141
 
0.6%
7saygdee3p 1120
 
0.5%
7saygdee1p 1107
 
0.5%
Other values (12130) 193817
94.3%
2024-10-16T19:13:11.466430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 183682
 
8.9%
1 140620
 
6.8%
A 133056
 
6.5%
Y 113212
 
5.5%
J 96191
 
4.7%
P 95400
 
4.6%
5 90396
 
4.4%
D 84258
 
4.1%
3 83215
 
4.1%
G 81145
 
3.9%
Other values (24) 953215
46.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2054390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 183682
 
8.9%
1 140620
 
6.8%
A 133056
 
6.5%
Y 113212
 
5.5%
J 96191
 
4.7%
P 95400
 
4.6%
5 90396
 
4.4%
D 84258
 
4.1%
3 83215
 
4.1%
G 81145
 
3.9%
Other values (24) 953215
46.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2054390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 183682
 
8.9%
1 140620
 
6.8%
A 133056
 
6.5%
Y 113212
 
5.5%
J 96191
 
4.7%
P 95400
 
4.6%
5 90396
 
4.4%
D 84258
 
4.1%
3 83215
 
4.1%
G 81145
 
3.9%
Other values (24) 953215
46.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2054390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 183682
 
8.9%
1 140620
 
6.8%
A 133056
 
6.5%
Y 113212
 
5.5%
J 96191
 
4.7%
P 95400
 
4.6%
5 90396
 
4.4%
D 84258
 
4.1%
3 83215
 
4.1%
G 81145
 
3.9%
Other values (24) 953215
46.4%

County
Text

Distinct205
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size10.7 MiB
2024-10-16T19:13:12.004777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.498895
Min length3

Characters and Unicode

Total characters1129671
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)< 0.1%

Sample

1st rowKitsap
2nd rowKitsap
3rd rowKing
4th rowKing
5th rowThurston
ValueCountFrequency (%)
king 105237
50.6%
snohomish 24721
 
11.9%
pierce 16197
 
7.8%
clark 12231
 
5.9%
thurston 7526
 
3.6%
kitsap 6848
 
3.3%
spokane 5460
 
2.6%
whatcom 4978
 
2.4%
benton 2546
 
1.2%
skagit 2266
 
1.1%
Other values (210) 19940
 
9.6%
2024-10-16T19:13:13.011371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 162985
14.4%
n 159733
14.1%
K 113202
10.0%
g 108337
9.6%
o 74994
 
6.6%
h 63698
 
5.6%
a 52211
 
4.6%
s 46748
 
4.1%
e 45852
 
4.1%
r 41113
 
3.6%
Other values (42) 260798
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1129671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 162985
14.4%
n 159733
14.1%
K 113202
10.0%
g 108337
9.6%
o 74994
 
6.6%
h 63698
 
5.6%
a 52211
 
4.6%
s 46748
 
4.1%
e 45852
 
4.1%
r 41113
 
3.6%
Other values (42) 260798
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1129671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 162985
14.4%
n 159733
14.1%
K 113202
10.0%
g 108337
9.6%
o 74994
 
6.6%
h 63698
 
5.6%
a 52211
 
4.6%
s 46748
 
4.1%
e 45852
 
4.1%
r 41113
 
3.6%
Other values (42) 260798
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1129671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 162985
14.4%
n 159733
14.1%
K 113202
10.0%
g 108337
9.6%
o 74994
 
6.6%
h 63698
 
5.6%
a 52211
 
4.6%
s 46748
 
4.1%
e 45852
 
4.1%
r 41113
 
3.6%
Other values (42) 260798
23.1%

City
Text

Distinct770
Distinct (%)0.4%
Missing3
Missing (%)< 0.1%
Memory size11.2 MiB
2024-10-16T19:13:13.956238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.2139839
Min length3

Characters and Unicode

Total characters1687448
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique262 ?
Unique (%)0.1%

Sample

1st rowSeabeck
2nd rowBremerton
3rd rowSeattle
4th rowSeattle
5th rowYelm
ValueCountFrequency (%)
seattle 33328
 
14.0%
bellevue 10235
 
4.3%
redmond 7341
 
3.1%
vancouver 7286
 
3.1%
bothell 6769
 
2.8%
kirkland 5998
 
2.5%
renton 5986
 
2.5%
sammamish 5908
 
2.5%
island 5568
 
2.3%
olympia 4966
 
2.1%
Other values (807) 145119
60.8%
2024-10-16T19:13:15.317575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 230045
13.6%
a 163122
 
9.7%
l 149516
 
8.9%
t 116445
 
6.9%
n 112430
 
6.7%
o 100238
 
5.9%
r 70390
 
4.2%
i 66733
 
4.0%
S 57061
 
3.4%
d 55856
 
3.3%
Other values (42) 565612
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1687448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 230045
13.6%
a 163122
 
9.7%
l 149516
 
8.9%
t 116445
 
6.9%
n 112430
 
6.7%
o 100238
 
5.9%
r 70390
 
4.2%
i 66733
 
4.0%
S 57061
 
3.4%
d 55856
 
3.3%
Other values (42) 565612
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1687448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 230045
13.6%
a 163122
 
9.7%
l 149516
 
8.9%
t 116445
 
6.9%
n 112430
 
6.7%
o 100238
 
5.9%
r 70390
 
4.2%
i 66733
 
4.0%
S 57061
 
3.4%
d 55856
 
3.3%
Other values (42) 565612
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1687448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 230045
13.6%
a 163122
 
9.7%
l 149516
 
8.9%
t 116445
 
6.9%
n 112430
 
6.7%
o 100238
 
5.9%
r 70390
 
4.2%
i 66733
 
4.0%
S 57061
 
3.4%
d 55856
 
3.3%
Other values (42) 565612
33.5%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
WA
204997 
CA
 
116
VA
 
58
MD
 
32
TX
 
26
Other values (40)
 
210

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters410878
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 204997
99.8%
CA 116
 
0.1%
VA 58
 
< 0.1%
MD 32
 
< 0.1%
TX 26
 
< 0.1%
CO 17
 
< 0.1%
NC 16
 
< 0.1%
IL 13
 
< 0.1%
AZ 13
 
< 0.1%
FL 11
 
< 0.1%
Other values (35) 140
 
0.1%

Length

2024-10-16T19:13:15.808652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 204997
99.8%
ca 116
 
0.1%
va 58
 
< 0.1%
md 32
 
< 0.1%
tx 26
 
< 0.1%
co 17
 
< 0.1%
nc 16
 
< 0.1%
il 13
 
< 0.1%
az 13
 
< 0.1%
fl 11
 
< 0.1%
Other values (35) 140
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 205221
49.9%
W 204999
49.9%
C 168
 
< 0.1%
V 67
 
< 0.1%
N 49
 
< 0.1%
M 47
 
< 0.1%
D 41
 
< 0.1%
T 40
 
< 0.1%
L 37
 
< 0.1%
I 34
 
< 0.1%
Other values (15) 175
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 410878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 205221
49.9%
W 204999
49.9%
C 168
 
< 0.1%
V 67
 
< 0.1%
N 49
 
< 0.1%
M 47
 
< 0.1%
D 41
 
< 0.1%
T 40
 
< 0.1%
L 37
 
< 0.1%
I 34
 
< 0.1%
Other values (15) 175
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 410878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 205221
49.9%
W 204999
49.9%
C 168
 
< 0.1%
V 67
 
< 0.1%
N 49
 
< 0.1%
M 47
 
< 0.1%
D 41
 
< 0.1%
T 40
 
< 0.1%
L 37
 
< 0.1%
I 34
 
< 0.1%
Other values (15) 175
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 410878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 205221
49.9%
W 204999
49.9%
C 168
 
< 0.1%
V 67
 
< 0.1%
N 49
 
< 0.1%
M 47
 
< 0.1%
D 41
 
< 0.1%
T 40
 
< 0.1%
L 37
 
< 0.1%
I 34
 
< 0.1%
Other values (15) 175
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct929
Distinct (%)0.5%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98177.972
Minimum1731
Maximum99577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:16.288220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1731
5-th percentile98007
Q198052
median98125
Q398372
95-th percentile99001
Maximum99577
Range97846
Interquartile range (IQR)320

Descriptive statistics

Standard deviation2419.0375
Coefficient of variation (CV)0.02463931
Kurtosis963.32419
Mean98177.972
Median Absolute Deviation (MAD)101
Skewness-30.211164
Sum2.016929 × 1010
Variance5851742.3
MonotonicityNot monotonic
2024-10-16T19:13:16.759275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 5203
 
2.5%
98012 3876
 
1.9%
98033 3417
 
1.7%
98006 3252
 
1.6%
98188 3205
 
1.6%
98004 3184
 
1.5%
98115 3067
 
1.5%
98074 2829
 
1.4%
98072 2746
 
1.3%
98034 2646
 
1.3%
Other values (919) 172011
83.7%
ValueCountFrequency (%)
1731 2
< 0.1%
1749 1
< 0.1%
2116 1
< 0.1%
2827 1
< 0.1%
2842 2
< 0.1%
3804 1
< 0.1%
6335 1
< 0.1%
6340 1
< 0.1%
6355 1
< 0.1%
6365 1
< 0.1%
ValueCountFrequency (%)
99577 1
 
< 0.1%
99403 71
 
< 0.1%
99402 12
 
< 0.1%
99362 406
0.2%
99361 13
 
< 0.1%
99360 8
 
< 0.1%
99357 22
 
< 0.1%
99356 1
 
< 0.1%
99354 325
0.2%
99353 275
0.1%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.9604
Minimum1997
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:17.136345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2015
Q12019
median2022
Q32023
95-th percentile2024
Maximum2025
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9890589
Coefficient of variation (CV)0.0014790289
Kurtosis0.8392647
Mean2020.9604
Median Absolute Deviation (MAD)1
Skewness-1.2144026
Sum4.1518408 × 108
Variance8.9344729
MonotonicityNot monotonic
2024-10-16T19:13:17.504848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 60161
29.3%
2024 30030
14.6%
2022 28465
13.9%
2021 19837
 
9.7%
2018 14386
 
7.0%
2020 12241
 
6.0%
2019 10872
 
5.3%
2017 8662
 
4.2%
2016 5474
 
2.7%
2015 4741
 
2.3%
Other values (12) 10570
 
5.1%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1999 4
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 22
 
< 0.1%
2010 24
 
< 0.1%
2011 707
 
0.3%
2012 1549
 
0.8%
2013 4331
2.1%
ValueCountFrequency (%)
2025 415
 
0.2%
2024 30030
14.6%
2023 60161
29.3%
2022 28465
13.9%
2021 19837
 
9.7%
2020 12241
 
6.0%
2019 10872
 
5.3%
2018 14386
 
7.0%
2017 8662
 
4.2%
2016 5474
 
2.7%

Make
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
TESLA
90318 
CHEVROLET
15114 
NISSAN
14525 
FORD
10840 
KIA
9104 
Other values (37)
65538 

Length

Max length20
Median length14
Mean length5.5576059
Min length3

Characters and Unicode

Total characters1141749
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOYOTA
2nd rowNISSAN
3rd rowTESLA
4th rowNISSAN
5th rowCHEVROLET

Common Values

ValueCountFrequency (%)
TESLA 90318
44.0%
CHEVROLET 15114
 
7.4%
NISSAN 14525
 
7.1%
FORD 10840
 
5.3%
KIA 9104
 
4.4%
BMW 8481
 
4.1%
TOYOTA 7945
 
3.9%
HYUNDAI 5782
 
2.8%
RIVIAN 5679
 
2.8%
VOLKSWAGEN 5638
 
2.7%
Other values (32) 32013
 
15.6%

Length

2024-10-16T19:13:17.930346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 90318
43.9%
chevrolet 15114
 
7.4%
nissan 14525
 
7.1%
ford 10840
 
5.3%
kia 9104
 
4.4%
bmw 8481
 
4.1%
toyota 7945
 
3.9%
hyundai 5782
 
2.8%
rivian 5679
 
2.8%
volkswagen 5638
 
2.7%
Other values (37) 32169
 
15.6%

Most occurring characters

ValueCountFrequency (%)
E 153128
13.4%
A 151132
13.2%
S 138026
12.1%
T 124562
10.9%
L 124091
10.9%
O 61902
 
5.4%
I 52362
 
4.6%
N 51119
 
4.5%
R 45521
 
4.0%
V 36254
 
3.2%
Other values (18) 203652
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1141749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 153128
13.4%
A 151132
13.2%
S 138026
12.1%
T 124562
10.9%
L 124091
10.9%
O 61902
 
5.4%
I 52362
 
4.6%
N 51119
 
4.5%
R 45521
 
4.0%
V 36254
 
3.2%
Other values (18) 203652
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1141749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 153128
13.4%
A 151132
13.2%
S 138026
12.1%
T 124562
10.9%
L 124091
10.9%
O 61902
 
5.4%
I 52362
 
4.6%
N 51119
 
4.5%
R 45521
 
4.0%
V 36254
 
3.2%
Other values (18) 203652
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1141749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 153128
13.4%
A 151132
13.2%
S 138026
12.1%
T 124562
10.9%
L 124091
10.9%
O 61902
 
5.4%
I 52362
 
4.6%
N 51119
 
4.5%
R 45521
 
4.0%
V 36254
 
3.2%
Other values (18) 203652
17.8%

Model
Text

Distinct152
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size10.9 MiB
2024-10-16T19:13:18.909626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.4274282
Min length2

Characters and Unicode

Total characters1320438
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowRAV4 PRIME
2nd rowLEAF
3rd rowMODEL 3
4th rowLEAF
5th rowBOLT EV
ValueCountFrequency (%)
model 89680
27.5%
y 43437
13.3%
3 32113
 
9.9%
leaf 13488
 
4.1%
bolt 9492
 
2.9%
s 7881
 
2.4%
ev 7518
 
2.3%
x 6249
 
1.9%
prime 6090
 
1.9%
volt 4829
 
1.5%
Other values (150) 104791
32.2%
2024-10-16T19:13:20.405346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 153754
11.6%
L 132001
 
10.0%
O 127861
 
9.7%
120130
 
9.1%
M 106168
 
8.0%
D 98873
 
7.5%
A 56243
 
4.3%
R 49143
 
3.7%
Y 47883
 
3.6%
I 46315
 
3.5%
Other values (28) 382067
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1320438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 153754
11.6%
L 132001
 
10.0%
O 127861
 
9.7%
120130
 
9.1%
M 106168
 
8.0%
D 98873
 
7.5%
A 56243
 
4.3%
R 49143
 
3.7%
Y 47883
 
3.6%
I 46315
 
3.5%
Other values (28) 382067
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1320438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 153754
11.6%
L 132001
 
10.0%
O 127861
 
9.7%
120130
 
9.1%
M 106168
 
8.0%
D 98873
 
7.5%
A 56243
 
4.3%
R 49143
 
3.7%
Y 47883
 
3.6%
I 46315
 
3.5%
Other values (28) 382067
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1320438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 153754
11.6%
L 132001
 
10.0%
O 127861
 
9.7%
120130
 
9.1%
M 106168
 
8.0%
D 98873
 
7.5%
A 56243
 
4.3%
R 49143
 
3.7%
Y 47883
 
3.6%
I 46315
 
3.5%
Other values (28) 382067
28.9%

E.V_Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
BEV
161539 
PHEV
43900 

Length

Max length4
Median length3
Mean length3.2136887
Min length3

Characters and Unicode

Total characters660217
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHEV
2nd rowBEV
3rd rowBEV
4th rowBEV
5th rowBEV

Common Values

ValueCountFrequency (%)
BEV 161539
78.6%
PHEV 43900
 
21.4%

Length

2024-10-16T19:13:20.849094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:13:21.484128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bev 161539
78.6%
phev 43900
 
21.4%

Most occurring characters

ValueCountFrequency (%)
E 205439
31.1%
V 205439
31.1%
B 161539
24.5%
P 43900
 
6.6%
H 43900
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 660217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 205439
31.1%
V 205439
31.1%
B 161539
24.5%
P 43900
 
6.6%
H 43900
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 660217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 205439
31.1%
V 205439
31.1%
B 161539
24.5%
P 43900
 
6.6%
H 43900
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 660217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 205439
31.1%
V 205439
31.1%
B 161539
24.5%
P 43900
 
6.6%
H 43900
 
6.6%

CAFV
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
unknown
114172 
known
70016 
not eligible
21251 

Length

Max length12
Median length7
Mean length6.8355862
Min length5

Characters and Unicode

Total characters1404296
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowknown
2nd rowknown
3rd rowknown
4th rowknown
5th rowknown

Common Values

ValueCountFrequency (%)
unknown 114172
55.6%
known 70016
34.1%
not eligible 21251
 
10.3%

Length

2024-10-16T19:13:21.862372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:13:22.243852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 114172
50.4%
known 70016
30.9%
not 21251
 
9.4%
eligible 21251
 
9.4%

Most occurring characters

ValueCountFrequency (%)
n 503799
35.9%
o 205439
14.6%
k 184188
 
13.1%
w 184188
 
13.1%
u 114172
 
8.1%
e 42502
 
3.0%
l 42502
 
3.0%
i 42502
 
3.0%
t 21251
 
1.5%
21251
 
1.5%
Other values (2) 42502
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1404296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 503799
35.9%
o 205439
14.6%
k 184188
 
13.1%
w 184188
 
13.1%
u 114172
 
8.1%
e 42502
 
3.0%
l 42502
 
3.0%
i 42502
 
3.0%
t 21251
 
1.5%
21251
 
1.5%
Other values (2) 42502
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1404296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 503799
35.9%
o 205439
14.6%
k 184188
 
13.1%
w 184188
 
13.1%
u 114172
 
8.1%
e 42502
 
3.0%
l 42502
 
3.0%
i 42502
 
3.0%
t 21251
 
1.5%
21251
 
1.5%
Other values (2) 42502
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1404296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 503799
35.9%
o 205439
14.6%
k 184188
 
13.1%
w 184188
 
13.1%
u 114172
 
8.1%
e 42502
 
3.0%
l 42502
 
3.0%
i 42502
 
3.0%
t 21251
 
1.5%
21251
 
1.5%
Other values (2) 42502
 
3.0%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.164342
Minimum0
Maximum337
Zeros114172
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:22.661819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348
95-th percentile249
Maximum337
Range337
Interquartile range (IQR)48

Descriptive statistics

Standard deviation88.075859
Coefficient of variation (CV)1.6884304
Kurtosis1.2618468
Mean52.164342
Median Absolute Deviation (MAD)0
Skewness1.6481268
Sum10716173
Variance7757.357
MonotonicityNot monotonic
2024-10-16T19:13:23.069880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114172
55.6%
215 6426
 
3.1%
32 4895
 
2.4%
25 4429
 
2.2%
21 4220
 
2.1%
238 4007
 
2.0%
220 4004
 
1.9%
84 3845
 
1.9%
42 3054
 
1.5%
38 2524
 
1.2%
Other values (95) 53855
26.2%
ValueCountFrequency (%)
0 114172
55.6%
6 945
 
0.5%
8 38
 
< 0.1%
9 21
 
< 0.1%
10 167
 
0.1%
11 4
 
< 0.1%
12 166
 
0.1%
13 359
 
0.2%
14 1116
 
0.5%
15 89
 
< 0.1%
ValueCountFrequency (%)
337 86
 
< 0.1%
330 337
 
0.2%
322 1762
0.9%
308 507
 
0.2%
293 463
 
0.2%
291 2313
1.1%
289 655
 
0.3%
270 270
 
0.1%
266 1452
0.7%
265 128
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean922.67053
Minimum0
Maximum845000
Zeros202114
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:23.480681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7761.7536
Coefficient of variation (CV)8.41227
Kurtosis759.89611
Mean922.67053
Median Absolute Deviation (MAD)0
Skewness14.451484
Sum1.8954513 × 108
Variance60244819
MonotonicityNot monotonic
2024-10-16T19:13:23.871541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 202114
98.4%
69900 1359
 
0.7%
31950 365
 
0.2%
52900 220
 
0.1%
32250 138
 
0.1%
59900 128
 
0.1%
54950 124
 
0.1%
39995 116
 
0.1%
36900 103
 
0.1%
44100 99
 
< 0.1%
Other values (21) 665
 
0.3%
ValueCountFrequency (%)
0 202114
98.4%
31950 365
 
0.2%
32250 138
 
0.1%
32995 3
 
< 0.1%
33950 71
 
< 0.1%
34995 64
 
< 0.1%
36800 52
 
< 0.1%
36900 103
 
0.1%
39995 116
 
0.1%
43700 9
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 10
< 0.1%
110950 21
< 0.1%
109000 6
 
< 0.1%
102000 13
< 0.1%
98950 21
< 0.1%
91250 4
 
< 0.1%
90700 16
< 0.1%
89100 8
 
< 0.1%
81100 23
< 0.1%

Legislative District
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing442
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean28.970848
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:24.304013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median33
Q342
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.910052
Coefficient of variation (CV)0.51465706
Kurtosis-1.1197786
Mean28.970848
Median Absolute Deviation (MAD)12
Skewness-0.43916207
Sum5938937
Variance222.30964
MonotonicityNot monotonic
2024-10-16T19:13:24.765807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 12994
 
6.3%
45 12003
 
5.8%
48 11184
 
5.4%
1 8946
 
4.4%
5 8787
 
4.3%
11 8773
 
4.3%
36 8297
 
4.0%
46 7790
 
3.8%
43 7236
 
3.5%
37 5994
 
2.9%
Other values (39) 112993
55.0%
ValueCountFrequency (%)
1 8946
4.4%
2 2424
 
1.2%
3 1012
 
0.5%
4 1810
 
0.9%
5 8787
4.3%
6 2004
 
1.0%
7 973
 
0.5%
8 2158
 
1.1%
9 1171
 
0.6%
10 3566
 
1.7%
ValueCountFrequency (%)
49 2913
 
1.4%
48 11184
5.4%
47 3772
 
1.8%
46 7790
3.8%
45 12003
5.8%
44 5565
2.7%
43 7236
3.5%
42 2930
 
1.4%
41 12994
6.3%
40 4303
 
2.1%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct205439
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2771562 × 108
Minimum4469
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:25.233072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4469
5-th percentile1.1310642 × 108
Q11.9353244 × 108
median2.3823684 × 108
Q32.6187177 × 108
95-th percentile3.1901961 × 108
Maximum4.7925477 × 108
Range4.792503 × 108
Interquartile range (IQR)68339334

Descriptive statistics

Standard deviation72057372
Coefficient of variation (CV)0.31643579
Kurtosis3.6759381
Mean2.2771562 × 108
Median Absolute Deviation (MAD)27730682
Skewness0.32336659
Sum4.6781669 × 1013
Variance5.1922648 × 1015
MonotonicityNot monotonic
2024-10-16T19:13:25.702125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240684006 1
 
< 0.1%
249393770 1
 
< 0.1%
150024689 1
 
< 0.1%
261919150 1
 
< 0.1%
251776654 1
 
< 0.1%
235951292 1
 
< 0.1%
245853856 1
 
< 0.1%
186442210 1
 
< 0.1%
104376898 1
 
< 0.1%
259796652 1
 
< 0.1%
Other values (205429) 205429
> 99.9%
ValueCountFrequency (%)
4469 1
< 0.1%
4777 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
61092 1
< 0.1%
62261 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct928
Distinct (%)0.5%
Missing8
Missing (%)< 0.1%
Memory size15.5 MiB
2024-10-16T19:13:26.638214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length31
Mean length30.12852
Min length26

Characters and Unicode

Total characters6189332
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique351 ?
Unique (%)0.2%

Sample

1st rowPOINT (-122.8728334 47.5798304)
2nd rowPOINT (-122.6961203 47.5759584)
3rd rowPOINT (-122.3340795 47.6099315)
4th rowPOINT (-122.304356 47.715668)
5th rowPOINT (-122.5715761 46.9095798)
ValueCountFrequency (%)
point 205431
33.3%
47.6705374 5203
 
0.8%
122.1207376 5203
 
0.8%
122.206146 3876
 
0.6%
47.839957 3876
 
0.6%
122.1925969 3417
 
0.6%
47.676241 3417
 
0.6%
122.144149 3252
 
0.5%
47.560742 3252
 
0.5%
122.271716 3205
 
0.5%
Other values (1847) 376161
61.0%
2024-10-16T19:13:28.033023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 667489
 
10.8%
1 499897
 
8.1%
4 463491
 
7.5%
7 443922
 
7.2%
. 410862
 
6.6%
410862
 
6.6%
6 323630
 
5.2%
3 306731
 
5.0%
5 278284
 
4.5%
8 254825
 
4.1%
Other values (10) 2129339
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6189332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 667489
 
10.8%
1 499897
 
8.1%
4 463491
 
7.5%
7 443922
 
7.2%
. 410862
 
6.6%
410862
 
6.6%
6 323630
 
5.2%
3 306731
 
5.0%
5 278284
 
4.5%
8 254825
 
4.1%
Other values (10) 2129339
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6189332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 667489
 
10.8%
1 499897
 
8.1%
4 463491
 
7.5%
7 443922
 
7.2%
. 410862
 
6.6%
410862
 
6.6%
6 323630
 
5.2%
3 306731
 
5.0%
5 278284
 
4.5%
8 254825
 
4.1%
Other values (10) 2129339
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6189332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 667489
 
10.8%
1 499897
 
8.1%
4 463491
 
7.5%
7 443922
 
7.2%
. 410862
 
6.6%
410862
 
6.6%
6 323630
 
5.2%
3 306731
 
5.0%
5 278284
 
4.5%
8 254825
 
4.1%
Other values (10) 2129339
34.4%
Distinct74
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size18.3 MiB
2024-10-16T19:13:28.586577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.280545
Min length10

Characters and Unicode

Total characters9096818
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPUGET SOUND ENERGY INC
2nd rowPUGET SOUND ENERGY INC
3rd rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
4th rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 191315
12.5%
179062
11.7%
wa 125029
 
8.2%
tacoma 123304
 
8.0%
energy 123170
 
8.0%
sound 123170
 
8.0%
puget 122084
 
8.0%
inc||city 75156
 
4.9%
power 45573
 
3.0%
inc 42547
 
2.8%
Other values (112) 382547
25.0%
2024-10-16T19:13:29.565615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1327521
14.6%
O 668731
 
7.4%
N 652310
 
7.2%
T 629733
 
6.9%
A 611243
 
6.7%
E 596244
 
6.6%
I 499065
 
5.5%
C 494223
 
5.4%
Y 331453
 
3.6%
U 320263
 
3.5%
Other values (26) 2966032
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9096818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1327521
14.6%
O 668731
 
7.4%
N 652310
 
7.2%
T 629733
 
6.9%
A 611243
 
6.7%
E 596244
 
6.6%
I 499065
 
5.5%
C 494223
 
5.4%
Y 331453
 
3.6%
U 320263
 
3.5%
Other values (26) 2966032
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9096818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1327521
14.6%
O 668731
 
7.4%
N 652310
 
7.2%
T 629733
 
6.9%
A 611243
 
6.7%
E 596244
 
6.6%
I 499065
 
5.5%
C 494223
 
5.4%
Y 331453
 
3.6%
U 320263
 
3.5%
Other values (26) 2966032
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9096818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1327521
14.6%
O 668731
 
7.4%
N 652310
 
7.2%
T 629733
 
6.9%
A 611243
 
6.7%
E 596244
 
6.6%
I 499065
 
5.5%
C 494223
 
5.4%
Y 331453
 
3.6%
U 320263
 
3.5%
Other values (26) 2966032
32.6%

2020 Census Tract
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2163
Distinct (%)1.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2977042 × 1010
Minimum1.0010201 × 109
Maximum5.6021001 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-10-16T19:13:29.977566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0010201 × 109
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.303303 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6021001 × 1010
Range5.5019981 × 1010
Interquartile range (IQR)20063321

Descriptive statistics

Standard deviation1.5884355 × 109
Coefficient of variation (CV)0.029983468
Kurtosis758.2537
Mean5.2977042 × 1010
Median Absolute Deviation (MAD)28603
Skewness-27.122212
Sum1.0883392 × 1016
Variance2.5231272 × 1018
MonotonicityNot monotonic
2024-10-16T19:13:30.439463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10102598
 
1.3%
5.30330262 × 10101151
 
0.6%
5.303303232 × 1010905
 
0.4%
5.30330093 × 1010799
 
0.4%
5.30670112 × 1010761
 
0.4%
5.303303232 × 1010678
 
0.3%
5.30330228 × 1010660
 
0.3%
5.306105211 × 1010659
 
0.3%
5.303303222 × 1010648
 
0.3%
5.30330285 × 1010641
 
0.3%
Other values (2153) 195936
95.4%
ValueCountFrequency (%)
1001020100 3
< 0.1%
1081041901 1
 
< 0.1%
1089011028 1
 
< 0.1%
1097006803 1
 
< 0.1%
1101000900 2
< 0.1%
1101005503 1
 
< 0.1%
1117030613 1
 
< 0.1%
2020000206 1
 
< 0.1%
4013061052 1
 
< 0.1%
4013104802 1
 
< 0.1%
ValueCountFrequency (%)
5.60210011 × 10101
 
< 0.1%
5.50090205 × 10101
 
< 0.1%
5.307794001 × 10102
 
< 0.1%
5.307794001 × 10107
 
< 0.1%
5.307794001 × 10105
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.307794 × 10108
 
< 0.1%
5.307794 × 10108
 
< 0.1%
5.307794 × 10106
 
< 0.1%
5.30770034 × 101042
< 0.1%

Interactions

2024-10-16T19:13:01.717859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:48.845648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:50.398525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:51.884039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:53.605402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:55.881646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:58.824802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:02.124845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:49.060685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:50.618099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:52.131119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:53.810396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:56.324894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:59.185669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:02.507543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:49.282829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:50.815096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:52.378165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:54.019036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:56.748498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:59.591658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:02.883044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:49.515057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:51.031001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:52.621157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:54.289259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:57.214350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:00.030792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:03.246597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:49.736386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:51.235578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:52.865987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:54.671526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:57.593740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:00.390448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:03.653077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:49.943394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:51.437198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:53.113946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:55.080394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:58.012203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:01.006435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:04.015107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:50.162998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:51.631735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:53.357424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:55.475852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:12:58.409796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:13:01.342748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T19:13:30.744327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2020 Census TractBase MSRPCAFVDOL Vehicle IDE.V_TypeElectric RangeLegislative DistrictMakeModel YearPostal CodeState
2020 Census Tract1.0000.0000.0040.0060.007-0.013-0.1860.0000.0120.0630.982
Base MSRP0.0001.0000.023-0.0480.0180.1240.0090.080-0.180-0.0010.025
CAFV0.0040.0231.0000.3510.7460.6450.0590.5880.4920.0030.008
DOL Vehicle ID0.006-0.0480.3511.0000.073-0.175-0.0130.1180.451-0.0010.011
E.V_Type0.0070.0180.7460.0731.0000.5290.1140.7600.1750.0070.009
Electric Range-0.0130.1240.645-0.1750.5291.000-0.0120.364-0.6540.0670.010
Legislative District-0.1860.0090.059-0.0130.114-0.0121.0000.103-0.016-0.3301.000
Make0.0000.0800.5880.1180.7600.3640.1031.0000.1940.0000.000
Model Year0.012-0.1800.4920.4510.175-0.654-0.0160.1941.000-0.0550.000
Postal Code0.063-0.0010.003-0.0010.0070.067-0.3300.000-0.0551.0000.935
State0.9820.0250.0080.0110.0090.0101.0000.0000.0000.9351.000

Missing values

2024-10-16T19:13:04.715108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T19:13:06.499471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-16T19:13:08.438095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelE.V_TypeCAFVElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
0JTMAB3FV3PKitsapSeabeckWA98380.02023TOYOTARAV4 PRIMEPHEVknown42.00.035.0240684006POINT (-122.8728334 47.5798304)PUGET SOUND ENERGY INC5.303509e+10
11N4AZ1CP6JKitsapBremertonWA98312.02018NISSANLEAFBEVknown151.00.035.0474183811POINT (-122.6961203 47.5759584)PUGET SOUND ENERGY INC5.303508e+10
25YJ3E1EA4LKingSeattleWA98101.02020TESLAMODEL 3BEVknown266.00.043.0113120017POINT (-122.3340795 47.6099315)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
31N4AZ0CP8EKingSeattleWA98125.02014NISSANLEAFBEVknown84.00.046.0108188713POINT (-122.304356 47.715668)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+10
41G1FX6S00HThurstonYelmWA98597.02017CHEVROLETBOLT EVBEVknown238.00.020.0176448940POINT (-122.5715761 46.9095798)PUGET SOUND ENERGY INC5.306701e+10
55YJYGDEE5LSnohomishLynnwoodWA98036.02020TESLAMODEL YBEVknown291.00.021.0124511187POINT (-122.287143 47.812199)PUGET SOUND ENERGY INC5.306105e+10
6KM8S6DA23NKitsapPoulsboWA98370.02022HYUNDAISANTA FEPHEVknown31.00.023.0212217764POINT (-122.6368884 47.7469547)PUGET SOUND ENERGY INC5.303509e+10
77FCTGAAA1PSnohomishArlingtonWA98223.02023RIVIANR1TBEVunknown0.00.039.0252414039POINT (-122.11597 48.194109)PUGET SOUND ENERGY INC5.306105e+10
85YJYGDEE9LKingKentWA98031.02020TESLAMODEL YBEVknown291.00.047.0112668510POINT (-122.201564 47.402358)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
91N4AZ0CP2FKingKirklandWA98034.02015NISSANLEAFBEVknown84.00.045.0109765204POINT (-122.2026532 47.7210518)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelE.V_TypeCAFVElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
2054295YJYGDEF6MPierceUniversity PlaceWA98467.02021TESLAMODEL YBEVunknown0.00.028.0261718698POINT (-122.5325066 47.2051509)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY5.305307e+10
2054305YJ3E1EA2NWhatcomBlaineWA98230.02022TESLAMODEL 3BEVunknown0.00.042.0208668638POINT (-122.7318295 48.953176)CITY OF BLAINE - (WA)||PUD NO 1 OF WHATCOM COUNTY5.307301e+10
2054315YJ3E1EA4LSnohomishMarysvilleWA98270.02020TESLAMODEL 3BEVknown266.00.038.0127376686POINT (-122.1515174 48.0650751)PUGET SOUND ENERGY INC5.306105e+10
2054327SAYGDEE5PSpokaneNine Mile FallsWA99026.02023TESLAMODEL YBEVunknown0.00.06.0259908589POINT (-117.6098695 47.8047804)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY5.306301e+10
205433WA1E2AFY9PClarkVancouverWA98664.02023AUDIQ5 EPHEVnot eligible23.00.049.0240695682POINT (-122.575383 45.620105)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)5.301104e+10
2054341N4BZ1CV2NChelanPeshastinWA98847.02022NISSANLEAFBEVunknown0.00.012.0187384494POINT (-120.6051696 47.5510173)PUD NO 1 OF CHELAN COUNTY5.300796e+10
2054351FTVW1EV0PSnohomishEverettWA98208.02023FORDF-150BEVunknown0.00.044.0255036386POINT (-122.2032349 47.8956271)PUGET SOUND ENERGY INC5.306104e+10
2054365YJXCDE22HSpokaneCheneyWA99004.02017TESLAMODEL XBEVknown200.00.06.0221631588POINT (-117.5836098 47.4951312)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY5.306301e+10
2054375YJ3E1EA3JKingVashonWA98070.02018TESLAMODEL 3BEVknown215.00.034.0336983496POINT (-122.466938 47.429244)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
2054387SAYGDEE7PSpokaneSpokaneWA99208.02023TESLAMODEL YBEVunknown0.00.06.0228335040POINT (-117.4268937 47.7323627)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY5.306301e+10